
doi: 10.5281/zenodo.21423434 , 10.5281/zenodo.21466388 , 10.5281/zenodo.21253795 , 10.5281/zenodo.21260839 , 10.5281/zenodo.21423433 , 10.5281/zenodo.21417696 , 10.5281/zenodo.20810316 , 10.5281/zenodo.21260838 , 10.5281/zenodo.20810317 , 10.5281/zenodo.21466389 , 10.5281/zenodo.21417695 , 10.5281/zenodo.21253796
doi: 10.5281/zenodo.21423434 , 10.5281/zenodo.21466388 , 10.5281/zenodo.21253795 , 10.5281/zenodo.21260839 , 10.5281/zenodo.21423433 , 10.5281/zenodo.21417696 , 10.5281/zenodo.20810316 , 10.5281/zenodo.21260838 , 10.5281/zenodo.20810317 , 10.5281/zenodo.21466389 , 10.5281/zenodo.21417695 , 10.5281/zenodo.21253796
Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate whether English intermediate-task training is still helpful on non-English target tasks. Using nine intermediate language-understanding tasks, we evaluate intermediate-task transfer in a zero-shot cross-lingual setting on the XTREME benchmark. We see large improvements from intermediate training on the BUCC and Tatoeba sentence retrieval tas Research goal: Does intermediate-task training with multimodal models improve zero-shot cross-lingual transfer performance in multimodal understanding tasks, as measured by F1 scores on the X-MMR benchmark? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.3/10.
This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 9.3/10.
training, text-image, enhance, multimodal, intermediate-task, improve, models, zero-shot, impact, cross-lingual, combining, visual, image, tasks, question, retrieval, transfer, performance, text, image-captioning, answering
training, text-image, enhance, multimodal, intermediate-task, improve, models, zero-shot, impact, cross-lingual, combining, visual, image, tasks, question, retrieval, transfer, performance, text, image-captioning, answering
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